Finite element analysis of the shear performance of box-groove interface of ultra-high-performance concrete (UHPC)-normal strength concrete (NSC) composite girder

Farouk, Abdulwarith Ibrahim Bibi; Zhu, Jinsong; Yuhui, Gu

doi:10.1007/s41062-022-00815-x

Cited by 14 publications

(3 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concrete damage plasticity (CDP) was used to simulate the material behavior of UHPC and NSC. The constitutive model used by Farouk et al [10] defined the uniaxial compression and tension of the concretes and steel bars. The CDPM parameters for NSC and UHPC used in this study are similar to those used by the authors in [11].…”

Section: Generalmentioning

confidence: 99%

Compressive Behavior of Normal Strength Concrete Column Strengthened With Reinforced Ultra-High-Performance Concrete

Ibrahim,

Haruna,

Farouk

et al. 2024

World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

Ultra-high-performance concrete (UHPC) and normal strength concrete (NSC) are concrete materials with high compatibility characteristics. This paper presents the experimental and numerical study on the uniaxial compression behavior of NSC columns strengthened using UHPC. The main idea of the proposed strengthening technique is to optimize the UHPC and enhance the ultimate capacity, impermeability, and crack resistance of the damaged column. Fourteen ( 14) NSC columns strengthened with UHPC were tested. The influence of longitudinal groove geometry and volumetric ratio of shear reinforcement on the failure mode, ultimate capacity, displacement, initial stiffness, ductility index, and ultimate strain of the composite column was investigated. Increasing the groove size will increase the axial resistance and improve the ductility of the strengthened NSC column. Decreasing the shear reinforcement ratio does not affect the axial strength of the column. The developed FEM can accurately simulate the behavior of the column strengthened with UHPC using the proposed technique.

show abstract

Section: Generalmentioning

confidence: 99%

Compressive Behavior of Normal Strength Concrete Column Strengthened With Reinforced Ultra-High-Performance Concrete

Ibrahim,

Haruna,

Farouk

et al. 2024

World Congress on Civil, Structural, and Environmental Engineering

View full text Add to dashboard Cite

show abstract

“…Similarly, the contact surface associated with the NC-PUG composite was used as the master surface. A friction coefficient of 0.4 was used between the two components [47]. The normal contact behavior was assumed to be hard contact, allowing for the transmission of the surface's pressure and separation when the pressure was zero or negative.…”

Section: Contact Modelingmentioning

confidence: 99%

Flexural Response of Concrete Specimen Retrofitted with PU Grout Material: Experimental and Numerical Modeling

Haruna,

Ibrahim,

Han

et al. 2023

Polymers

Self Cite

View full text Add to dashboard Cite

Polyurethane (PU) composite is increasingly used as a repair material for civil engineering infrastructure, including runway, road pavement, and buildings. Evaluation of polyurethane grouting (PUG) material is critical to achieve a desirable maintenance effect. This study aims to evaluate the flexural behavior of normal concrete repaired with polyurethane grout (NC-PUG) under a three-point bending test. A finite element (FE) model was developed to simulate the flexural response of the NC-PUG specimens. The equivalent principle response of the NC-PUG was analyzed through a three-dimensional finite element model (3D FEM). The NC and PUG properties were simulated using stress–strain relations acquired from compressive and tensile tests. The overlaid PUG material was prepared by mixing PU and quartz sand and overlayed on the either top or bottom surface of the concrete beam. Two different overlaid thicknesses were adopted, including 5 mm and 10 mm. The composite NC-PUG specimens were formed by casting a PUG material using different overlaid thicknesses and configurations. The reference specimen showed the highest average ultimate flexural stress of 5.56 MPa ± 2.57% at a 95% confidence interval with a corresponding midspan deflection of 0.49 mm ± 13.60%. However, due to the strengthened effect of the PUG layer, the deflection of the composite specimen was significantly improved. The concrete specimens retrofitted at the top surface demonstrated a typical linear pattern from the initial loading stage until the complete failure of the specimen. Moreover, the concrete specimens retrofitted at the bottom surface exhibit two deformation regions before the complete failure. The FE analysis showed good agreement between the numerical model and the experimental test result. The numerical model accurately predicted the flexural strength of the NC-PUG beam, slightly underestimating Ke by 4% and overestimating the ultimate flexural stress by 3%.

show abstract

“…Conducting push out tests is one of the main methods for assessing the performance of stud connectors, but it requires a lot of time and money. Finite element simulation is widely used for load carrying capacity calculations, but it requires a high degree of specialization and the output results are highly variable (Farouk et al, 2022(Farouk et al, , 2023. In order to provide a convenient calculation, a number of formulas have been proposed based on the results of experiments and finite element simulations, including calculations based on test results without considering the effect of damage modes (Luo et al, 2016;Zhang et al, 2020;Zhu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models and Gsa Method for Designing Stud Connectors

Sun,

Kang,

Shi

2024

Journal of Civil Engineering and Management

View full text Add to dashboard Cite

The design of stud connectors is aided by determining the relationship between shear strength and the input variables (number, diameter, height, tensile strength and elastic modulus of the studs, and compressive strength and elastic modulus of the concrete) that influence strength. Since strength is nonlinearly related to the influencing variables, which makes the predictions of the relevant empirical equations unreliable, the use of machine learning (ML) models is preferred. The prediction results of eight machine learning models were evaluated, including linear regression (LR1), ridge regression (RR), lasso regression (LR2), back-propagation artificial neural network (BP ANN), genetic algorithm optimized BP ANN (GA-BP ANN), extreme learning machines (ELM), random forests (RF), and support vector machines (SVM). The results show that the GA-BP ANN model is the most accurate model for prediction with a mean absolute percentage error (MAPE) of 6.17% and an R2 of 0.9599. Based on the GA-BP ANN model and the global sensitivity analysis (GSA) method, a new parameter importance analysis method was developed to compare the magnitude of the effect of different input variables on strength. It was found that stud diameter had the greatest effect on shear strength.

show abstract

Finite element analysis of the shear performance of box-groove interface of ultra-high-performance concrete (UHPC)-normal strength concrete (NSC) composite girder

Cited by 14 publications

References 42 publications

Compressive Behavior of Normal Strength Concrete Column Strengthened With Reinforced Ultra-High-Performance Concrete

Compressive Behavior of Normal Strength Concrete Column Strengthened With Reinforced Ultra-High-Performance Concrete

Flexural Response of Concrete Specimen Retrofitted with PU Grout Material: Experimental and Numerical Modeling

Application of Machine Learning Models and Gsa Method for Designing Stud Connectors

Contact Info

Product

Resources

About